When the PC industry was young we used to build computers from build-it-yourself computer kits. The best known kit was the Altair 8800 but another was actually called SOL (maybe due to the condition in which kit purchasers/PC hobbyists would find themselves…).

Nobody buys and builds computers from a kit anymore. We buy professional products that are built by people who design, assemble and test thousands of these every day. Yet the “build-it-yourself BI kit” is still the dominant way IT teams today buy, assemble and deliver Business Intelligence. That’s insane!

Since BI projects are built by hobbyists from tens of building blocks, it’s no surprise that their requirements so closely match “hobbyist” PC requirements:

More Important Requirements:Performance, cost, expandability, upgradeability.

Less Important Requirements:Reliability, availability, service, ergonomics and usability.

In an ideal world, BI Hobbyists would realize that assembling and operating a diverse set of technologies and products that come usually from number of suppliers (or from a single supplier through multiple acquisitions) would be overwhelmingly complex. But the fact that the pieces come from a large company typically gives the buyer the illusion of completeness and unwarranted optimism about the chance of success.

And we don’t live in an ideal world anyway. That’s why BI projects are so often delivered without the adequate “reliability, availability, service, ergonomics and usability”. And quite often they’re not delivered at all. Clearly, some BI hobbyists find themselves in similar position as PC kit purchasers used to…

These are the “S.O.L. moments” when we get calls from prospects today (increasingly from Fortune 500 types). As much as I would love to see GoodData as a part of large-scale BI projects from the very beginning, I understand that we need to prove ourselves first. I am more than happy to come to the rescue.

But isn’t it obvious to the industry that business intelligence should no longer be built by hobbyists? BI buyers should focus on business value (metrics, dashboards…) and BI projects should be built by people who design, test and deliver at least hundreds of these every day…

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Winston Churchill once said that “difficulties mastered are opportunities won”. His quote is very applicable to the the effort of building BI in the cloud. GoodData announced earlier today that May 2011 was our biggest month ever, so it is good time to look at difficulties and opportunities of BI Cloud in greater detail.

Business Intelligence is a huge opportunity. Even in its current, dysfunctional, on-premise form it is $10B software industry. And on-premise BI is extensive and expensive IT initiative that involves building a complete chain of data integration, data-warehousing, dashboarding and visualizations. On top of the IT efforts comes tricky business part: what to measure, what are the right metrics, how to present them and to whom. And it all has to happen at the speed of business, not at the speed of IT.

This IT/business dichotomy leads to extremely low success rate of BI projects – as much as $7 billion annually is spent on BI undertakings that are considered failures. That’s right – $7 billion worth of BI software ends up sitting on the shelf every year!

On the contrary the SaaS model works best when the product is well defined, customer adoption is fast, satisfaction/loyalty is high and cost of servicing the customer is low (for more information on SaaS metrics please read “Top 10 Laws of Cloud Computing and SaaS”here). This means that the traditional, slow moving, complex and expensive BI will NEVER make it to the cloud. Numerous small and large companies have tried to host their traditional on-premise BI products in the cloud, but SaaS laws are called laws for a reason – these companies either failed already or will eventually fail.

So what is GoodData doing differently to master the difficulties of Cloud BI?

1. Product Definition/Customer Adoption – in order to make customer adoption as quick as possible, we are building a set of BI applications. These apps are templates that contain not only connectors to standard data sources (such as Salesforce, Zendesk and Facebook) but also complete dashboards and reports that incorporate best practices in the form of metrics. Our Sales Analytics app helps you measure predicted revenue. Our Helpdesk Analytics app measures your backlog and resolution times. Our Marketing Analytics app teaches you how to calculate campaign ROI. We’re adding these applications on a weekly basis. You can see the full list of our apps here: http://www.gooddata.com/apps

2. Customer Loyalty – We deliver a complete, managed service to our customers. Our developers, ops and support personnel are making sure that every single data load goes as planned, all reports are loaded correctly and that there are no performance issues. We even publish our Operational & Service Performance here: http://www.gooddata.com/trust

3. Cost of Service – We’ve architected a very different platform that allows us to host a large number customers at a relatively low cost. The platform is so different that we often have a hard time communicating it to the BI analyst community (concepts like REST APIs and stateless services are not part of normal BI nomenclature). And the flexibility built into the platform allows us to move at the pace of business and not the pace of IT: we deliver a new version of GoodData to our customers every two weeks and we make tons of changes to customer projects daily.

Even the fact that we know how many reports we served to our customers in May of 2011 (over 1,000,000) sets us apart. While the old BI industry can only guess the level of adoption and product usage (of shelfware) we actually know. But again, “difficulties mastered are opportunities won”!

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Peter Yared wrote recently a BusinessWeek guest blog post called “Failure of Commercial Open Source Software.” Not surprisingly his post caused a lot of angry replies from people who work for COSS companies. “The emperor is not naked” they argued.

I believe that the COSS emperor is openly naked. And the discussion shouldn’t be whether COSS is a complete or a partial failure just because there are few successful exits that Peter neglected to mention. At the end of the day Peter’s comment that “selling software is miserable” is true. Every sales rep involved in selling COSS would agree (I’m interviewing many of them now). Selling COSS is no easier than selling any other form of software.

Any company using the word “open” should be able to explain the true cost of delivery (this is one of Peter’s points). And there is an obvious litmus test of openness of COSS companies: One that I would call “open pricing.” COSS companies should openly publish their price list and clearly mark what’s free and open and what’s paid and closed. Otherwise OSS is just a bait-and-switch to a familiar proprietary software tactic of customer lock-in. This is what OSS was supposed to get rid of in the first place.

Let’s take a look at some of COSS companies in the Business Intelligence space. The bait and switch is in a full swing here:

We announced GoodData pricing earlier today and I would actually argue that we are a more open company than any of companies listed above. Our customers know exactly what service they get and how much it will cost.

We stick to our company motto: GoodData = BI – BS. And at there is a lot of BS going on in COSS space. It may actually be its biggest failure.

Full disclosure: I have been a big believer in open source since we opensourced NetBeans more than 10 years ago.

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Back in the old good days of enterprise software, we did not need to worry about our customers. We delivered bits on DVDs – it was up to the customers to struggle with installation, integration, management, customization and other aspects of software operations. We collected all the cash upfront, took another 25% in annual maintenance. Throwing software over the wall … that’s how we did it. Sometimes almost literally…

I now live in the SaaS world. My customers only pay us if we deliver a service level consistent with our SLAs. We are responsible for deployment, security, upgrades and so on. We operate software for our customers and we deliver it as service.

But there now seems to be a new way how to “throw software over the wall” again. Many software companies have repackaged their software as Amazon Machine Image (AMI) and relabeled them as SaaS or Cloud Computing. It’s so simple, it’s so clever: Dear customer, here is the image of our database, server, analytical engine, ETL tool, integration bus, dashboard etc. All you need it is go to AWS, get an account and start those AMIs. Scaling, integration, upgrades is your worry again. Welcome back to the world of enterprise software…

AMI is the new DVD and this approach to cloud computing is the worst thing that could happen to SaaS. And SaaS in my vocabulary is still Software as a Service…

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No other major SaaS company in the world could get away with this approach to paying customers. Not only Google offers no user-friendly tools to add shared contact to the paid version of Google Apps. They offer no tools. Period.

Here is the only information available to email administrators:

Administrative management of non-employee contacts now available

Premier Edition administrators can now add contacts that aren’t employees of their own company to the contact list that each user can access in the new standalone contact manager.

First, create an XML representation of the shared contact to publish. This XML needs to be in the form of an Atom element of the Contact kind, which might look like this:

To publish this entry, send it to the contact-list feed URL as follows. First, place your Atom element in the body of a new POST request, using the application/atom+xml content type. Then send it to the feed URL. For example, to add a domain shared contact to the list belonging to example.com, post the new entry to the following URL:

The Google server creates a contact using the entry you sent, then returns an HTTP 201 CREATED status code, along with a copy of the new contact in the form of an element. The entry returned is the same one you sent, but it also contains various elements added by the server, such as an element.

If your request fails for some reason, Google may return a different status code. For information about the status codes, see the Google Data API protocol reference document.

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I believe that readers of my blog are familiar with the chart below. It is the classical “Crossing the chasm” diagram from Geoffrey A. Moore’s book Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers. This book was published back in 1991 and it is still number two on “Twelve Business Books in One Hour for the Busy CEO” list. The main argument that Geoffrey Moore makes here is that the “early adopters” have no ability to influence “early majority” and it leads to a chasm that is very difficult for startups (or any company with disruptive technology) to cross:

This book was written in the days when startups typically sold to electrical engineers (a.k.a. IT) and Brad Burnham described it well in his recent post:

In the old days, electrical engineers focused on getting computers to work not on getting people to engage with the systems built on top of those computers. The folks that built enterprise software were vaguely aware that their systems had to be accessible to the humans that used them but they had a huge advantage. The people who used them did so as part of their job, they were trained to use them and fired if they could not figure them out.

This is why even Wikipedia uses the following example to describe the end-user:

The end-user or consumer may differ from the person who purchases the product. For instance, a zookeeper, the customer, might purchase elephant food for an end-user: the elephant.

But virtually no startup gets funded today if it sells directly to electrical engineers. Innovation happens in the consumer space anyway and so the assumption is that any disruptive technology (social networks, SaaS, web2.0…) gets adopted first by the end-users and then it is picked up by the IT. But here comes the new chasm: low ability of end-users to influence IT:

This chasm is the new manifestation of the classical “Business-IT” gap but this time is the innovation flow reversed: business leads and IT follows. And this new flow doesn’t make it any easier to cross the new chasm…

We made the following announcement earlier today and it is obviously a very important milestones for us. And I absolutely believe in what I said in the press release: “Intelimedix’s expertise, combined with Good Data’s on demand collaborative analytics, form an unbeatable combination for healthcare organizations,” said Roman Stanek, founder and CEO of Good Data Corp. “This is a great opportunity to show how solution providers benefit from incorporating Good Data into their offerings.”

Leading Healthcare Information Provider Licenses Good Data for On Demand Business Intelligence

CAMBRIDGE, Mass. – December 2, 2008 – Good Data Corporation, an emerging provider of on-demand (SaaS) collaborative business intelligence solutions, today announced its first customer agreement – with Intelimedix LLC, a leading supplier of business intelligence solutions for health insurers.

Good Data, which recently completed a $2 million initial round of funding from private investors, delivers a cloud-based platform for business intelligence projects. The company is launching a public beta of its hosted service in December 2008 that will offer data analysts in any company immediate and inexpensive access to the power of collaborative business intelligence.

Good Data helps Intelimedix enhance its core analytic service offerings. Intelimedix plans to integrate Good Data capabilities into core applications that run reporting and analysis tools for functions including payment integrity, fraud detection, benchmarking, and measuring operational efficiency.

“In the healthcare environment, users need to be able to access information quickly, efficiently and reliably to make strategic decisions that impact their business,” said David Robinson, Chief Technology Officer of Intelimedix. “Good Data’s technology will help us improve our analytical tools immeasurably. We see Good Data as an important strategic partner that will help us deliver more flexible, effective solutions to our customers.”

“Intelimedix’s expertise, combined with Good Data’s on demand collaborative analytics, form an unbeatable combination for healthcare organizations,” said Roman Stanek, founder and CEO of Good Data Corp. “This is a great opportunity to show how solution providers benefit from incorporating Good Data into their offerings.”

About Good Data
Good Data Corporation was founded with the mission to provide a platform for collaborative analytics. The company believes sharing and teamwork allows users to move past isolated reports and arrive at the true meaning of “business intelligence.” Development of the underlying technology began in 2002 and is currently in use in large insurance and retail corporations. Good Data is a privately held company with headquarters in Cambridge, Mass., and engineering operations in the Czech Republic.

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The creation of Good Data was very much influenced by Tim O’Reilly’s post “What Would Google Do?” published back in May 2007. The following idea still defines my vision for Good Data and the need for collaborative BI:
If Google or Amazon were your bank or credit card, they’d let you know which merchants had the best prices for the same products, so you’d be a smarter shopper next time. They’d let merchants know what products were popular with people who also bought related products. They’d help merchants stock the right products by zip code. They’d let you know when you were spending more on dining out than you have set in your family budget. They’d let you know when you were approaching your credit limit, with a real-time fuel gauge, not just a “Sorry, your card has been declined.”

The notion that companies will share data and analytics beyond their firewalls and this shared knowledge will help them to build better businesses might have interesting implications:

Historically companies needed to buy other companies to be able to share knowledge and management processes and this was the only way how to increase their value. But if you read a post called A Lost Decade – But Not For Everyone written by Fred Wilson, you will realize that companies that tried to be present in every market and own every possible aspect of their business landscape delivered no (or negative) return to their investors over the last ten years. Fred calls this “the end of the industrial era and the emergence of the information era”. And Jeff Jarvis explores the trend even more in his recent Guardian column:
In a sense, Google itself is built on a derivative: its data on data. Like the derivatives that got us into this mess, Google’s are based on creating abundance. But unlike those corrupted financial products, Google’s metaknowledge creates new and real value.

In Google’s economy, small is the new big. Of course, big is still big — Google itself is gargantuan. But it doesn’t grow by borrowing capital to buy companies (likely no one will for some time to come). Instead, Google created a network for an abundance of new advertisers and a platform for countless new businesses, all independent of Google. Indeed, Google does not want to own the assets — content to commerce — upon which its empire is built.

I believe that good data is the capital of the information era and in the future everybody will have to do what Google does…

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I intentionally mixed two concepts in the title of this article. The first one is the concept of Internet platform as defined by Marc Andreessen here. And the second one is the Global SOA: the non-visual data and services portion of the World Wide Web. So what does it take to make Good Data a Level 1 platform and make it a good SOA citizen? Here is my list of interfaces:

Upstream APIs (REST/Atom):

Data integration processes access the following APIs to manage the data/metadata flow from the transactional systems into the hosted datawarehouse

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Can you agree and disagree with someone at the same time? The quote below comes from Rob Ashe, general manager of IBM’s business intelligence and performance management unit:

BI doesn’t lend itself to SaaS. Every company is different because even if transaction systems are the same the decision making process is different. Unlike Netsuite or a CRM application where everyone does the same basic things, BI uses a different model every company. The one to many model doesn’t work.

And while I agree with Rob that it’s the “intelligence” part that makes hosted BI difficult it is not impossible to provide BI as a service. Capturing the data model and sharing it with users will be a core piece of Good Data…